I’ve used it in an HFT system. Basically as a form of latency-minimizing model compression for evaluation in production.

The problem was that the base layer model (tree ensembles) did very well in terms of training error but was much too slow to evaluate in production. With LSH, you can start with a bunch of points from the base model, then train the compressed model against the reference samples.